[Keras] Three ways to use custom validation metrics in Keras

Keras offers some basic metrics to validate the test data set like accuracy, binary accuracy or categorical accuracy. However, sometimes other metrics are more feasable to evaluate your model. In this post I will show three different approaches to apply your cusom metrics in Keras.

Simple callbacks

The simplest one is described in the official Keras documentation. It is basically just a measure, which accepts the true values and the predictions: